amazon-far/holosoma

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1616 stars · 249 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Holosoma is a Python framework for training and deploying reinforcement learning policies on humanoid robots, supporting locomotion and whole-body tracking tasks across multiple simulators like IsaacGym, IsaacSim, MJWarp, and MuJoCo. It also includes motion retargeting to convert human motion capture data into robot motions and a shared inference pipeline for sim-to-sim and sim-to-real deployment.

Use cases

• train RL locomotion policies for humanoid robots
• train whole-body tracking policies from motion capture
• retarget human mocap data to robot motions
• deploy trained policies to real humanoid robots
• evaluate policies in MuJoCo simulation
• log training videos and checkpoints with Wandb

When to choose

• you are training RL policies for Unitree G1 or Booster T1 humanoids
• you need multi-simulator support (IsaacGym, IsaacSim, MJWarp, MuJoCo)
• you want an end-to-end pipeline from mocap retargeting to real-robot deployment
• you want PPO or FastSAC with shared sim/real inference

When to avoid

• you work with non-humanoid robots
• you need Windows or macOS support
• you only need simple robot simulation without RL training
• you cannot access NVIDIA Isaac simulators or GPUs

Facets

framework · maturity active

reinforcement-learning simulation robotics machine-learning llm-training robotics reinforcement-learning simulation machine-learning python humanoid-robots motion-retargeting sim-to-real isaacgym mujoco ppo fastsac unitree-g1 whole-body-tracking locomotion linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="amazon-far/holosoma")